Breast cancer is one of the leading causes of death in women. Regular testing is of paramount importance since early detection ensures more treatment options and better prognosis rate. Biopsies, x-rays and ultrasounds...
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ISBN:
(数字)9798331538538
ISBN:
(纸本)9798331538545
Breast cancer is one of the leading causes of death in women. Regular testing is of paramount importance since early detection ensures more treatment options and better prognosis rate. Biopsies, x-rays and ultrasounds are some of the diagnostic tools used to detect breast cancer. Out of these, ultrasounds being portable and ionizing radiation free provide the most feasible and harmless solution in areas which cannot accommodate expensive equipment that require rigorous safety standards to be met. Our detection system utilizes Vision Transformer (ViT) technology, i.e., transfer learning to classify tumors of the breast into malignant, benign and normal categories. Data enhancement has been done using Contrast Limited Adaptive Histogram Equalization (CLAHE). Furthermore, we have categorized them based on BI-RADS criteria for greater accuracy and precision scores. We have chosen ViT over the more commonly used Convolution Neural Networks (CNN) since they offer a more flexible approach, capturing global relationships in images, and you can use it for larger datasets.
Air quality monitoring is gaining increasing importance as awareness about the health impacts of air pollution continues to grow. These monitors typically track air pollutants to give users a clearer picture of their ...
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ISBN:
(数字)9798331531195
ISBN:
(纸本)9798331531201
Air quality monitoring is gaining increasing importance as awareness about the health impacts of air pollution continues to grow. These monitors typically track air pollutants to give users a clearer picture of their environment. Accurate and real-time monitoring increases our understanding of the surrounding air we breathe and allows us to improve it. The proposed system helps users minimize their exposure to harmful pollutants by providing real-time data on the concentration of various gases. The system triggers alerts directly to the subscribed user’s mobile phones in case of any hazardous situations so that the user can take appropriate actions immediately to resolve the issue. Our extensive data collection and evaluation with off-the-shelf machine learning models indicated an overall F1-score of 99.54 (±0.5)% with a simple random forest model in classifying burning, cooking, occupancy in a closed room, using aerosol products, based on the sensor’s readings.
Plant diseases require quick and accurate identification methods since they significantly reduce agricultural production's output and quality. Traditional methods of plant protection, which depend on visual assess...
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ISBN:
(数字)9798331512965
ISBN:
(纸本)9798331512972
Plant diseases require quick and accurate identification methods since they significantly reduce agricultural production's output and quality. Traditional methods of plant protection, which depend on visual assessment of pests and diseases that impact soyabean plants, have long turnaround times and variable accuracy. A critical need as the demand for precision agriculture grows is the creation of quick, effective, and—above all—computer-aided disease recognition systems. This study proposed convolutional neural network-based classification model for soyabean leaf. Proposed model can detect nine different types of soyabean diseases. Our transfer-learning based model classifies soybean leaves using a Inception B0 convolutional neural network, and on unseen testing data, we obtain balanced accuracies between 91.11% and 96.44%. The result of proposed model is generated on 3000 different images of soyabean leaves and classify nine different types of soyabean diseases.
This study employs machine learning, historical analysis, and natural language processing (NLP) to deconstruct the recurring lethal stampedes at India’s mass religious gatherings, focusing on the 2025 Mahakumbh trage...
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Text-to-Image (T2I) synthesis is a challenging task that requires modeling complex interactions between two modalities (, i.e., text and image). A common framework adopted in recent state-of-the-art approaches to achi...
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Multimodal sentiment analysis plays a critical role in numerous IoT-driven applications, such as personalized smart assistants, healthcare monitoring systems, and intelligent transportation networks, where accurate in...
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Microservices architecture has become the foundation of modern application development, offering enhanced scalability, flexibility, and maintainability. This paper presents a comparative performance evaluation of two ...
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ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
Microservices architecture has become the foundation of modern application development, offering enhanced scalability, flexibility, and maintainability. This paper presents a comparative performance evaluation of two widely used JavaScript-based stacks, MERN (MongoDB, Express, React, ***) and MEAN (MongoDB, Express, Angular, ***), deployed as microservices applications in Minikube, a local Kubernetes environment. While both stacks share a common backend technology stack, they differ in their front-end frameworks—React in MERN and Angular in MEAN—which influences their performance, scalability, and suitability for different application types. This study not only compares the architectural differences between MERN and MEAN but also introduces machine learning (ML)-based microservice optimization to enhance service instance selection and resource allocation in Kubernetes. The optimization leverages RF (Random Forest) and XGBoost ML algorithms for dynamic intelligent POD selection and scheduling, ensuring dynamic routing of microservice requests based on real-time performance metrics. Key evaluation criteria include latency, response time, CPU and memory utilization, scalability, and fault tolerance, monitored using Prometheus and Istio. The results provide insights into the efficiency of each stack in handling microservices workloads, helping developers and system architects choose the most suitable stack based on performance demands, resource constraints, and deployment complexity. The paper concludes with recommendations for optimizing microservices-based applications in Kubernetes environments using ML-driven orchestration strategies.
The conventional method of tender sanctioning involves a process whereby tenders are awarded to companies that offer the most efficient and economical solutions according to the requirements of the government or the p...
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ISBN:
(数字)9798331525439
ISBN:
(纸本)9798331525446
The conventional method of tender sanctioning involves a process whereby tenders are awarded to companies that offer the most efficient and economical solutions according to the requirements of the government or the providing organization. However, the selection criteria for these companies are often shrouded in secrecy, lacking transparency in the allocation of tenders to specific bidding parties. To address this issue, we propose a blockchain-based authentication scheme incorporating different consensus mechanisms at various stages of the tender sanctioning process. This approach ensures a secure and transparent method of tender sanctioning. Previously we have implemented our concept in another aspect of bank operations, specifically in the sanctioning of loans to eligible customers of the bank. By utilizing our proposed method, we establish a transparent process for loan sanctioning within the bank. Furthermore, we conduct a comparative analysis to evaluate the effectiveness of our proposed method across two distinct applications. This analysis involves comparing the efficacy of various consensus mechanisms, such as Proof of Work and Proof of Authority, in both scenarios.
This research examines the influence of regularity of routine and quality of sleep on mental well-being, providing evidence-based solutions for these basic relationships. From the data of 376 participants, the stronge...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
This research examines the influence of regularity of routine and quality of sleep on mental well-being, providing evidence-based solutions for these basic relationships. From the data of 376 participants, the strongest predictors of mental well-being were determined to be regularity of routine and ideal duration of sleep ranging from 7–9 hours. Mental wellbeing states were classified using machine learning techniques, and XGBoost provided an accuracy of 88.20%. The results offer practical advice on how to improve mental functioning by situating healthy practices and adequate rest at the core as key drivers for building mental toughness.
Cryptocurrencies have quickly become a significant part of the financial landscape, largely due to their potential for substantial returns. This study examines a trading model based on Deep Q-Network (DQN) that combin...
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ISBN:
(数字)9798331525439
ISBN:
(纸本)9798331525446
Cryptocurrencies have quickly become a significant part of the financial landscape, largely due to their potential for substantial returns. This study examines a trading model based on Deep Q-Network (DQN) that combines reinforcement learning with a hybrid decision-making approach. Unlike traditional RL models that treat buy, hold, and sell decisions as equal, this framework differentiates the decision-making process. It enables DQN to learn optimal strategies for buying and holding while using a rule-based mechanism focused on profit targets for selling. This hybrid method ensures the model effectively captures market trends and secures profits at set thresholds, thereby minimizing unnecessary risk exposure. By adapting to changing market conditions and automating trade execution, this approach seeks to enhance profitability while reducing the risks associated with cryptocurrency trading. Experimental results highlight the effectiveness of this method in volatile market conditions
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